CN114168474A

CN114168474A - Automated test case generation method, apparatus, device, medium, and program product

Info

Publication number: CN114168474A
Application number: CN202111502304.4A
Authority: CN
Inventors: 戴威
Original assignee: China Construction Bank Corp
Current assignee: China Construction Bank Corp
Priority date: 2021-12-09
Filing date: 2021-12-09
Publication date: 2022-03-11

Abstract

The disclosure provides an automatic test case generation method, which can be applied to the technical field of testing. The automatic test case generation method comprises the following steps: s1, extracting the sample test cases to obtain first keywords based on the extraction units with preset granularity; s2, performing first analysis on the first training test case according to the first keyword; s3, extracting the ith training test case which fails to be analyzed for the (i + 1) th time to obtain the (i + 1) th key word; s4, performing i +1 th analysis on the i +1 th training test case according to the i +1 th keyword; s5, the steps S3-S4 are circulated until the proportion of the training test cases with failed analysis is less than the threshold value, and a keyword set is obtained; s7, analyzing the full test cases based on the keyword set to obtain the automatic test cases. The present disclosure also provides an automated test case generation apparatus, a device, a storage medium, and a program product.

Description

Automated test case generation method, apparatus, device, medium, and program product

Technical Field

The present disclosure relates to the field of testing, and more particularly, to an automated testing method, apparatus, device, medium, and program product.

Background

In order to reduce the difficulty degree of editing an automatic test case, a common method of the existing automatic test framework is to encapsulate a function into keywords, use the names of the keywords to explain the function targets of the function, and use the keywords and data combination mode to edit the automatic test case by a tester; and secondly, when the tester selects the operation object, the code automatically completes interface analysis and automatic test case editing. In the first method, the packaging accuracy of the keywords may have a great influence on the actual execution and the post-maintenance of the automated test case. In the second method, an automation framework for editing an automation test case is completed according to interface analysis, and functions are not open and cannot be expanded autonomously; the degree of customization of the tool is high, and testers need to strictly execute the tool according to an operation method; the requirement on test equipment is high.

Disclosure of Invention

In view of the foregoing, embodiments of the present disclosure provide an efficient and flexible automated test case generation method, apparatus, device, medium, and program product.

According to a first aspect of the present disclosure, an automated test case generation method is provided, which is characterized by including the following steps: s1, performing first extraction on a sample test case based on an extraction unit with preset granularity to obtain a first keyword; s2, analyzing the first training test case for the first time according to the first keyword; s3, performing extraction for the (i + 1) th training test case which fails in analysis for (i + 1) th time to obtain an (i + 1) th key word, wherein the extraction for the (i + 1) th time is performed based on an extraction unit with preset granularity, and i is a positive integer greater than or equal to 1; s4, analyzing the (i + 1) th training test case for (i + 1) th time according to the (i + 1) th keyword; s5, circularly executing the steps S3-S4 until the proportion of the nth training test case which fails to be analyzed in the nth training test case is smaller than a preset threshold value; s6, when the proportion of the training test case with failed analysis in the n training test case is smaller than a preset threshold value, acquiring a keyword set, wherein the keyword set comprises the n keyword, n is the number of times of extraction when the proportion of the training test case with failed analysis in the current training test case is smaller than the preset threshold value, and n is a positive integer greater than or equal to i + 1; and S7, analyzing the full test cases based on the keyword set to obtain the automatic test cases.

According to an embodiment of the present disclosure, the analyzing the first training test case according to the first keyword includes: analyzing the first training test case into a case keyword name and a case input value based on the first keyword, wherein the first keyword comprises the first keyword name and a first keyword input value type; executing a first judgment, wherein the first judgment comprises judging whether the case keyword name is matched with the first keyword name; and executing a second judgment when the case keyword name is matched with the first keyword name, wherein the second judgment comprises judging whether the type of the case input value is matched with that of the first keyword input value, and when the first judgment and the second judgment are both matched successfully, determining that the case analysis is successful.

According to the embodiment of the disclosure, the i +1 th time of extraction of the training test case to obtain the i +1 th key word includes: analyzing the type of the failure reason based on the ith training test case with analysis failure; and performing extraction on the training test case with the analysis failure for the (i + 1) th time based on the failure reason type and a corresponding supplementary extraction method to obtain an (i + 1) th keyword.

According to the embodiment of the disclosure, when the type of the failure cause includes that the training test case with the analysis failure contains a functional module with a larger difference from the functional module corresponding to the ith keyword, the (i + 1) th extraction includes: and (4) complementarily extracting the (i + 1) th key word on the basis of the functional module with failed analysis.

According to the embodiment of the disclosure, when the type of the cause of the analysis failure includes that the keyword name of the training test case with the analysis failure matches with the ith keyword name, but the case data thereof does not match with the ith keyword input value type, the extraction of the (i + 1) th includes: and adjusting the keyword input type or a special rule of the newly added keyword input type to obtain the (i + 1) th keyword.

According to the embodiment of the disclosure, when the type of the cause of the analysis failure includes unsuccessful analysis, but the training test case of the analysis failure contains a functional module with a smaller difference from the functional module corresponding to the ith keyword, the extracting by the (i + 1) th step includes: and adding the ith keyword for fuzzy matching to form a keyword name group so as to obtain the (i + 1) th keyword.

According to the embodiment of the disclosure, before analyzing the full number of test cases based on the keyword set and obtaining the automatic test cases, the method further comprises the following steps; and establishing a mapping relation between the keyword set and the automatic test codes, and acquiring an automatic test script.

According to an embodiment of the present disclosure, analyzing the full number of test cases based on the keyword set to obtain the automated test cases includes: recording analysis failure case information; calculating the ratio of the number of analysis failure cases to the number of analysis completion cases; and when the ratio is greater than or equal to a preset threshold value, manually maintaining the keyword set.

According to an embodiment of the present disclosure, when the ratio is smaller than the preset threshold, the method further includes: and archiving the analyzed full test cases, and performing irregular manual sampling inspection.

According to an embodiment of the present disclosure, the performing a first extraction on the sample test cases in an extraction unit with a preset granularity includes: and when the sample test case statement corresponds to a single page, performing first extraction by taking the sample test case statement as an extraction unit to obtain a first keyword.

According to an embodiment of the present disclosure, the performing the first extraction on the sample test case based on the extraction unit with the preset granularity further includes: when the sample test case sentences correspond to m pages, extracting m sub-keywords by taking the sentence part corresponding to each page as a first extraction unit; and packaging the m sub-keywords to obtain a first keyword corresponding to the sample test case statement, wherein m is a positive integer greater than or equal to 1.

A second aspect of the present disclosure provides an automated test case generation apparatus, including: the first extraction module is configured to perform first extraction on the sample test case based on an extraction unit with preset granularity to obtain a first keyword; the first analysis module is configured to analyze the first training test case for the first time according to the first keyword; the second extraction module is configured to perform extraction for the (i + 1) th training test case with failed analysis for (i + 1) th key words, wherein the extraction for the (i + 1) th time is performed based on an extraction unit with preset granularity, and i is a positive integer greater than or equal to 1; the second analysis module is configured to perform i +1 th analysis on the i +1 th training test case according to the i +1 th keyword; the circulation module is configured to execute the steps in the second extraction module and the second analysis module in a circulation manner until the ratio of the n training test case with failed analysis in the n training test case is smaller than a preset threshold, wherein n is the extraction times when the ratio of the training test case with failed analysis in the current training test case is smaller than the preset threshold, and n is a positive integer greater than or equal to i + 1; the system comprises a keyword acquisition module and an acquisition module, wherein the keyword acquisition module is configured to acquire a keyword set when the proportion of a training test case which fails to be analyzed in an nth training test case is less than a preset threshold value, and the acquisition module is configured to analyze a whole number of test cases based on the keyword set to acquire an automatic test case.

A third aspect of the present disclosure provides an electronic device, comprising: one or more processors; memory for storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the above-described method.

A fourth aspect of the present disclosure also provides a computer-readable storage medium having stored thereon executable instructions that, when executed by a processor, cause the processor to perform the above-described method.

A fifth aspect of the disclosure also provides a computer program product comprising a computer program which, when executed by a processor, implements the above method.

Drawings

The foregoing and other objects, features and advantages of the disclosure will be apparent from the following description of embodiments of the disclosure, which proceeds with reference to the accompanying drawings, in which:

fig. 1 schematically illustrates an application scenario diagram of an automated test case generation method according to an embodiment of the present disclosure.

Fig. 2 schematically shows a flow chart of an automatic test case generation method according to an embodiment of the present disclosure.

Fig. 3 schematically illustrates a flow chart of a method of first extracting a sample test case according to an embodiment of the present disclosure.

Fig. 4 schematically illustrates a flow chart of a method of first extracting a sample test case according to further embodiments of the present disclosure.

Fig. 5 schematically illustrates a flowchart of a method of parsing the first training test case according to a first keyword according to an embodiment of the present disclosure.

Fig. 6 schematically shows a flowchart of a method for performing an i +1 th extraction on an i training test case to obtain an i +1 th keyword according to an embodiment of the present disclosure.

Fig. 7 schematically shows a flowchart of a method for analyzing a full number of test cases based on the keyword set to obtain automated test cases.

Fig. 8 schematically shows a block diagram of an automated test case generation apparatus according to an embodiment of the present disclosure.

Fig. 9 schematically illustrates a block diagram of an electronic device suitable for implementing an automated test case generation method according to an embodiment of the present disclosure.

Detailed Description

Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is illustrative only and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present disclosure.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.

All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It is noted that the terms used herein should be interpreted as having a meaning that is consistent with the context of this specification and should not be interpreted in an idealized or overly formal sense.

Where a convention analogous to "at least one of A, B and C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B and C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.).

It should be noted that the method, the apparatus, the device, the medium, and the program product for generating an automatic test case provided in the embodiments of the present disclosure may be applied to the field of test technologies in the aspects related to the generation of the automatic test case, and may also be applied to various fields other than the field of test technologies, such as the financial field. The application fields of the automatic test case generation method, the automatic test case generation device, the automatic test case generation equipment, the automatic test case generation medium and the automatic test case generation program product provided by the embodiment of the disclosure are not limited.

The embodiment of the disclosure provides an automatic test case generation method, which is characterized by comprising the following steps: s1, performing first extraction on a sample test case based on an extraction unit with preset granularity to obtain a first keyword; s2, analyzing the first training test case for the first time according to the first keyword; s3, performing extraction for the (i + 1) th training test case which fails in analysis for (i + 1) th time to obtain an (i + 1) th key word, wherein the extraction for the (i + 1) th time is performed based on an extraction unit with preset granularity, and i is a positive integer greater than or equal to 1; s4, analyzing the (i + 1) th training test case for (i + 1) th time according to the (i + 1) th keyword; s5, circularly executing the steps S3-S4 until the proportion of the nth training test case which fails to be analyzed in the nth training test case is smaller than a preset threshold value; s6, when the proportion of the training test case which fails to be analyzed in the nth training test case is smaller than a preset threshold value, acquiring a keyword set, wherein the keyword set comprises the nth keyword; and S7, analyzing the full test cases based on the keyword set to obtain the automatic test cases, wherein n is the extraction times of the training test cases with failed analysis when the ratio of the training test cases in the current training test cases is less than a preset threshold, and n is a positive integer greater than or equal to i + 1.

As shown in fig. 1, the application scenario 100 according to this embodiment may include

terminal devices

101, 102, 103, a network 104 and a server 105. The network 104 serves as a medium for providing communication links between the

terminal devices

101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.

The user may use the

terminal devices

101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. The

terminal devices

101, 102, 103 may have installed thereon various communication client applications, such as shopping-like applications, web browser applications, search-like applications, instant messaging tools, mailbox clients, social platform software, etc. (by way of example only).

The

terminal devices

101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.

The server 105 may be a server providing various services, such as a background management server (for example only) providing support for websites browsed by users using the

terminal devices

101, 102, 103. The background management server may analyze and perform other processing on the received data such as the user request, and feed back a processing result (e.g., a webpage, information, or data obtained or generated according to the user request) to the terminal device.

It should be noted that the automatic test case generation method provided by the embodiments of the present disclosure may be generally executed by the server 105. Accordingly, the automatic test case generation apparatus provided by the embodiments of the present disclosure may be generally disposed in the server 105. The automatic test case generation method provided by the embodiments of the present disclosure may also be performed by a server or a server cluster that is different from the server 105 and is capable of communicating with the

terminal devices

101, 102, 103 and/or the server 105. Accordingly, the automatic test case generating apparatus provided by the embodiment of the present disclosure may also be disposed in a server or a server cluster different from the server 105 and capable of communicating with the

terminal devices

101, 102, 103 and/or the server 105.

It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.

The automatic test case generation method of the disclosed embodiment will be described in detail below with reference to fig. 2 to 7 based on the scenario described in fig. 1.

As shown in fig. 2, the automatic test case generation method of this embodiment includes operations S210 to S270.

In operation S210, a first extraction is performed on the sample test case based on an extraction unit with a preset granularity, and a first keyword is obtained.

According to an embodiment of the present disclosure, the first keyword is a keyword obtained from a sample test case. In the embodiments of the present disclosure, the sample test case may be selected in units of functional modules. The function module may be selected based on the coarsest granularity used by a general tester, for example, the function module may be a front-end program product, such as a main line function module in a Web application and a desktop. In a typical example, the front-end program product may be internet banking, and the function module may be a transfer function in the internet banking. It is understood that the main line function module may include a plurality of scenario modules, and in this case, in order to improve the accuracy of keyword extraction, a single scenario in the main line function may be further used as the function module, for example, a public-to-public account of the transfer function in internet banking is used as the function module. By selecting test cases in different scenes, a sample test case can be obtained, i.e., the sample test case can be a case set of test cases including multiple scenes. The number of the test cases in each application scene can be selected according to the requirement of accuracy of the keywords and the requirement of data processing capacity. Accordingly, the first keyword may be a set of keywords, which may contain one or more keywords. Each key input may contain a null, single-valued, single-set-valued, etc. pattern. The "empty" may correspond to no input box, and typically may include a form of input by way of a drop-down box, a check, and the like. A "single value" may correspond to the form of input content comprising one parameter, a typical single value may be "query reply content", a single set of values may correspond to the form of input content comprising two or more parameters, a typical single set of values comprising a "username | password".

It can be understood that the sample test cases are generally composed of sentences, and each sentence case sentence can correspond to an operation flow of one page in one functional module; one case statement can also correspond to the operation flows of a plurality of pages in one functional module. In order to further improve the accuracy of keyword extraction and improve the robustness of the automated test case generation method according to the embodiment of the disclosure, the method of fig. 3 or fig. 4 may be combined to preset extraction unit granularity, and the sample test case is subjected to first extraction to obtain the first keyword.

As shown in fig. 3, the method of performing the first extraction on the sample test cases of this embodiment includes operation S310.

In operation S310, when the sample test case statement corresponds to a single page, a first extraction is performed by using the sample test case statement as an extraction unit to obtain a first keyword.

According to embodiments of the present disclosure, when the sample test case statement completely corresponds to a single page, it is better close to the testing habit of the general testing personnel, so that the first keyword extraction can be performed with the single statement as the extraction unit.

According to other embodiments of the present disclosure, when the sample test case statement corresponds to multiple pages, in order to achieve better granular packing, double packing may be performed to extract the first key.

As shown in fig. 4, the method for performing the first extraction on the sample test case of this embodiment includes operations S410 to S420.

In operation S410, when the sample test case sentences correspond to m pages, m sub-keywords are extracted with the sentence part corresponding to each page as a first extraction unit.

In operation S420, the m sub-keywords are encapsulated to obtain a first keyword corresponding to the sample test case statement.

According to other embodiments of the present disclosure, when the sample test case statement corresponds to m pages (m is a positive integer greater than or equal to 1), in order to achieve better granularity encapsulation, the sample test case statement may be divided according to a portion corresponding to a single page to obtain extraction subunits, and a keyword is extracted for each extraction subunit to obtain m sub-keywords corresponding to m pages, which is a repackage; the m sub-keywords are repackaged to obtain the first keyword corresponding to the single sample test case statement, which is a double package. Through double packaging, the friendliness of testers is improved, and the keyword maintenance is facilitated.

In operation S220, a first training test case is parsed according to the first keyword.

As shown in fig. 5, the method for parsing the first training test case according to the first keyword according to the embodiment includes operations S510 to S530.

In operation S510, the training test case is parsed into a case keyword name and a case input value based on the first keyword, wherein the first keyword includes the first keyword name and a first keyword input value type.

In operation S520, performing a first judgment, which includes judging whether the case keyword name and the first keyword name match; and

in operation S530, when the case keyword name and the first keyword name match, a second determination is performed, which includes determining whether the case input value and the first keyword input value type match.

According to the embodiment of the disclosure, when the first judgment and the second judgment are both matched successfully, it is determined that the case analysis is successful.

According to an embodiment of the present disclosure, the first keyword includes a first keyword name and a first keyword input value type. The first training test case may be parsed into a combination of case key names and case input values. And judging whether the analysis is successful or not based on the matching degree of the case keyword name and the first key word name and the matching degree of the case input value and the first key word input value type. A typical example is that a first training test case has a statement "login operator ABC, password 123456", which takes the affixes of keyword name matching statements one by one in the extracted first keyword (actually a keyword set containing multiple keywords); when the name of the 'login' keyword is successfully matched, extracting fields 'ABC' and '123456' with the same type in subsequent sentences as the input value of the keyword according to the type of the first keyword input value which is well defined, such as { user name (type English) (special rule: the field of the user name can not contain) | password (type number) }; and the part which can not complete matching is a matching failure part, and finally an analysis result is generated. The special rules can be set according to the testing habits of the testers, so that the influence of case writing style differences on analysis results is reduced, and the effectiveness of the automatic test case generation method of the embodiment of the disclosure is improved. For example, according to the above-described typical example, when the case statement does not contain the username field, the parsing can still be achieved due to the existence of the special rule. It should be noted that, in order to perform the analysis more conveniently, the first training test case conforming to the universal test case template may be selected.

In the embodiment of the present disclosure, the parsing languages that can be used include, but are not limited to, JYTHON, PYTHON, JAVA, and the like, and the parsing languages of the embodiment of the present disclosure are not limited. The method of the embodiment of the disclosure is independent of a specific environment, and can be transplanted to each platform compatible with the language of the analytic training test case and cooperatively operated with other functional modules.

In operation S230, an i +1 th time of extraction is performed on the ith training test case that fails to be analyzed, and an i +1 th key word is obtained, where the i +1 th time of extraction is performed based on an extraction unit with a preset granularity, and i is a positive integer greater than or equal to 1.

In operation S240, the (i + 1) th training test case is analyzed for the (i + 1) th time according to the (i + 1) th keyword.

In operation S250, it is determined whether the occupation ratio of the n-th training test case failing to be analyzed in the n-th training test case is smaller than a preset threshold, where n is the number of times of extraction when the occupation ratio of the training test case failing to be analyzed in the current training test case is smaller than the preset threshold, and n is a positive integer greater than or equal to i + 1. When the proportion of the nth training test case which fails to be analyzed in the nth training test case is greater than the preset threshold value, executing the steps S230-S240 in a circulating manner;

in operation S260, when the proportion of the training test case failing to be analyzed in the nth training test case is less than the preset threshold, a keyword set is obtained, where the keyword set includes the nth keyword.

According to the embodiment of the disclosure, in order to improve the accuracy of keyword extraction, the idea of deep learning can be used for reference, the test cases are divided into the sample test cases and the training test cases, the keywords extracted from the sample test cases are used for analyzing the training test cases, the keywords are extracted again from the training test cases which fail to be analyzed based on the analysis result, and the training test cases corresponding to the functional modules are added to verify the accuracy of the keywords extracted again until the keyword set meeting the preset accuracy judgment condition is obtained. The selection method of the training test case can be approximately the same as that of the sample test case, and the main difference is that the number of the training test case and the sample test case is different for the same functional module. For example, for a certain functional module, one case may be selected as a sample test case, and other multiple cases for the same functional module may be selected as training test cases.

In some embodiments, after the first keyword is extracted based on the sample test case, the first training test case may be subjected to matching analysis to verify the accuracy of the first keyword. For some cases with failed parsing, a second extraction may be performed to obtain a second keyword. It will be appreciated that, for a better understanding of the test cases, at the time of the second extraction, all of the extraction units in the case of failed parsing may be analyzed to extract the second key, which may be an addition or a modification to the part of the first key that failed parsing. The granularity setting of the extraction unit can be the same as that of the first extraction, and can also be adjusted based on the actual training situation and the feedback of the testing personnel to ensure that the granularity setting is close to the testing habit of the common testing personnel. Furthermore, after the second keyword is obtained, the original training test case with failed analysis can be analyzed again, the training test case corresponding to the same functional module can be reselected for analysis, and the training test case with the same functional module can be supplemented for analysis on the basis of the original training test case with failed analysis. In the embodiment of the present disclosure, the redefined training test case is used as a second training test case, and the second training test case is analyzed by using a second keyword.

According to the embodiment of the disclosure, the extraction process of the second keyword and the analysis process of the second training test case can be executed circularly, that is, the ith training test case with analysis failure is extracted for the (i + 1) th time in an iterative manner, so as to obtain the (i + 1) th keyword; and (5) carrying out i +1 th analysis on the i +1 th training test case according to the i +1 th keyword, wherein i is a positive integer greater than or equal to 1. In an embodiment of the present disclosure, the loop execution process may be executed n times until a ratio of the n-th training test case in which the analysis fails in the n-th training test case is less than a preset threshold, where n may be a positive integer greater than or equal to i + 1. At this time, a keyword set with higher accuracy is obtained, which includes the nth keyword, wherein the nth keyword may include a plurality of keywords having higher matching degree with each functional unit, which are close to the testing habit of the ordinary testing personnel and fit the writing habit of the testing case editor.

As shown in fig. 6, the extraction of the ith training test case for the (i + 1) th time and the obtaining of the (i + 1) th keyword according to the embodiment includes operations S610 to S620.

In operation S610, the failure cause type is analyzed based on the ith training test case in which the parsing fails.

In operation S620, the training test case with failed analysis is extracted for the (i + 1) th time based on the failure cause type and the complementary extraction method corresponding to the failure cause type, so as to obtain an (i + 1) th keyword.

According to the embodiment of the disclosure, before the preset threshold is reached, different complementary extraction methods can be adopted for extracting the next keyword based on different analysis failure reason types.

In some specific embodiments, when the training test case with the failure cause type including the parsing failure contains a functional module with a larger difference from a functional module corresponding to the ith keyword, the (i + 1) th extraction may include: and (4) complementarily extracting the (i + 1) th key word on the basis of the functional module with failed analysis.

According to the specific embodiment of the disclosure, when the training test case with failed analysis contains a functional module with a larger difference from the functional module corresponding to the ith keyword, it indicates that the ith keyword is not enough to cover the functional module in the training test case, and at this time, a supplementary extraction method may be applied to complement the lack of the keyword. The supplementary extraction can be performed by using the uncovered function module, or by using the training test case containing the uncovered function module, which fails to be analyzed, or by supplementing the training test case containing the uncovered function module.

In some specific embodiments, when the type of cause of the failed parsing includes that the keyword name of the failed training test case matches the ith keyword name, but the case input value thereof does not match the ith keyword input value type, the extracting of the (i + 1) th includes: and adjusting the keyword input type or a special rule of the newly added keyword input type to obtain the (i + 1) th keyword.

According to the specific embodiment of the disclosure, when the case keyword name is matched with the ith keyword name but the case input value is not matched with the ith keyword input value type, it indicates that the defined ith keyword input value type is not enough to cover the input value representation method of the current case keyword, and at this time, the matching can be realized by adjusting the definition of the keyword input type or adding a new keyword input type special rule.

In some specific embodiments, when the type of cause of the parsing failure includes unsuccessful parsing, but the training test case with the parsing failure contains a functional module with a smaller difference from the functional module corresponding to the ith keyword, the extracting of the (i + 1) th includes: and adding the ith keyword for fuzzy matching to form a keyword name group so as to obtain the (i + 1) th keyword.

According to embodiments of the present disclosure, due to differences in habits of test case writers, different keywords may be employed in describing the same or similar functional modules. A typical example is that for the same "login" function, the keyword is "login", tester a is written as "login", tester B is written as "enter", and tester C is written as "input". At this time, if only the keyword "login" is extracted, it may cause that the keyword cannot be matched with the "enter", "input" modules with the same or similar functions. In the embodiment of the disclosure, a keyword name group can be formed by adding a keyword fuzzy matching mode, for example, "enter", "input", and "login" are packaged into a keyword name group, and keywords in the keyword name group are matched with affixes in training test cases one by one, so that the success rate of analysis is improved, and the maintenance cost of an automatic test case is reduced. It should be noted that when the newly added keywords are fuzzy and matched, the newly added keywords can be extracted from the training test case with failed analysis, and can also be added by collecting the feedback information of the tester or combining the experience of the developer. By introducing fuzzy matching to supplement the mapping relation between the affix of the training test case and the keyword, the reliability of mapping is enhanced.

In operation S270, the full number of test cases are analyzed based on the keyword set to obtain the automated test cases.

According to embodiments of the present disclosure, after a set of keywords is obtained, it may be put into the parsing of an automated test case. It can be understood that after training, the keyword set at least includes an nth keyword, where n is the number of times of extraction when the proportion of the training test case with failed parsing in the current training test case is less than a preset threshold, and n is a positive integer greater than or equal to i + 1. It is understood that the keyword set may further include the first keyword which is not included in the nth keyword and is successfully parsed, and the extracted keywords in the training test cases which are successfully parsed all the time. It will be appreciated that in some cases, the extracted first keyword has a higher success rate in parsing the first training test case. For example, the ratio of the first training test case with failed analysis in the full first training test case set is smaller than the preset threshold. At this time, the first keyword may be directly used as a keyword set, and the keyword set may be put into analysis of the whole number of test cases to generate the automated test case.

According to the embodiment of the disclosure, before analyzing the full test cases, the obtained keyword set can be used as codes to realize the code analysis, and the keyword set and the function association relation table are arranged. Specifically, for the keyword set obtained by training, the keyword names in the keyword set and function names can be associated, functions are further realized by using proper codes, and two-layer mapping (mapping relation is one-to-one correspondence) of a full test case, keywords and codes is completed to obtain an automatic test script, so that the full test case is directly applied to automatic test, and the threshold of test personnel for intervening in the automatic test is reduced.

As shown in fig. 7, the analyzing of the full number of test cases based on the keyword set and the acquiring of the automated test cases according to this embodiment includes operations S710 to S730.

In operation S710, resolution failure case information is recorded.

In operation S720, a ratio of the number of failed parsing cases to the number of completed parsing cases is calculated.

In operation S730, when the ratio is greater than or equal to a preset threshold, the keyword set is manually maintained.

According to the embodiment of the disclosure, after the full number of test cases are analyzed, the ratio of the number of analysis failed cases to the number of analysis completed cases can be calculated again to further select whether the keyword set needs to be manually maintained. The preset threshold may be determined based on the requirement for the success rate of analyzing the automated test cases and human resources, for example, the threshold may be adjusted lower when there are more resources, and vice versa when there are fewer resources. When the ratio is higher than the threshold, the maintenance personnel can intervene to extract the keywords again and analyze the training test case according to the keyword extraction and analysis method of the embodiment of the disclosure.

According to an embodiment of the present disclosure, when the ratio is smaller than the preset threshold, the method further includes: and archiving the analyzed full test cases, and performing irregular manual sampling inspection. The correctness of the analysis can be checked irregularly through manual sampling inspection. Meanwhile, when the adjustment of test case editors or the function changes in a large area, manual maintenance can be immediately started to improve the applicability of the keyword set.

According to the embodiment of the disclosure, the keyword set can be released and popularized to the testing personnel at irregular intervals, so that the subsequent test case editing is closer to an automatic translation system. For example, a test case editor can be guided to edit a test case by combining a keyword set, and for the situation that popularization is inconvenient, the test case editor also suggests that a tester edits in a single test case by taking the functional module of the embodiment of the disclosure as a unit to reduce the system maintenance cost.

According to the embodiment of the disclosure, the test case is directly analyzed according to the packaged keywords, and the threshold of the test personnel for intervention automatic test is reduced. The accuracy of keyword packaging is improved based on the deep learning algorithm, and the success rate of automatic testing is greatly improved. And the method can be applied to all software life cycles related to the automatic test, including development and debugging, user test, operation and maintenance green light production and the like, so that the technical barriers from non-automatic test developers to automatic test developers are reduced, the participation degree of the automatic test is improved, and the software research and development cost is reduced.

Based on the method, the invention also provides an automatic test case generation device. The apparatus will be described in detail below with reference to fig. 8.

As shown in fig. 8, the automated test case generating apparatus 800 of this embodiment includes a first extracting module 810, a first parsing module 820, a second extracting module 830, a second parsing module 840, a looping module 850, a keyword obtaining module 860, and a testing module 870.

The first extraction module 810 is configured to perform first extraction on the sample test cases based on an extraction unit with a preset granularity, and obtain a first keyword.

The first parsing module 820 is configured to pair the first training test case according to the first keyword.

The second extraction module 830 is configured to perform extraction (i + 1) th time on the ith training test case with failed analysis, and obtain an (i + 1) th key word, where the extraction (i + 1) th time is performed based on an extraction unit with a preset granularity, and i is a positive integer greater than or equal to 1.

The second parsing module 840 is configured to parse the (i + 1) th training test case for an (i + 1) th time according to the (i + 1) th keyword.

The loop module 850 is configured to loop the steps in the second extraction module and the second analysis module until the ratio of the n-th training test case with failed analysis in the n-th training test case is smaller than the preset threshold, where n is the number of extractions of the training test case with failed analysis in the current training test case, and n is a positive integer greater than or equal to i + 1.

The keyword obtaining module 860 is configured to obtain a keyword set when the proportion of the training test case with failed parsing in the nth training test case is less than a preset threshold, where the keyword set includes the nth keyword.

The testing module 870 is configured to parse a full number of test cases based on the set of keywords, obtaining automated test cases.

According to an embodiment of the present disclosure, any plurality of the first extraction module 810, the first parsing module 820, the second extraction module 830, the second parsing module 840, the circulation module 850, the keyword acquisition module 860, and the test module 870 may be combined into one module to be implemented, or any one of them may be split into a plurality of modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of the other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the first extraction module 810, the first parsing module 820, the second extraction module 830, the second parsing module 840, the loop module 850, the keyword obtaining module 860, and the test module 870 may be at least partially implemented as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented by hardware or firmware in any other reasonable manner of integrating or packaging a circuit, or implemented by any one of three implementations of software, hardware, and firmware, or any suitable combination of any of them. Alternatively, at least one of the first extraction module 810, the first parsing module 820, the second extraction module 830, the second parsing module 840, the loop module 850, the keyword acquisition module 860, and the test module 870 may be at least partially implemented as a computer program module that, when executed, may perform corresponding functions.

As shown in fig. 9, an electronic apparatus 900 according to an embodiment of the present disclosure includes a processor 901 which can perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)902 or a program loaded from a storage portion 908 into a Random Access Memory (RAM) 903. Processor 901 may comprise, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor and/or associated chipset, and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), among others. The processor 901 may also include on-board memory for caching purposes. The processor 901 may comprise a single processing unit or a plurality of processing units for performing the different actions of the method flows according to embodiments of the present disclosure.

In the RAM 903, various programs and data necessary for the operation of the electronic apparatus 900 are stored. The processor 901, the ROM 902, and the RAM 903 are connected to each other through a bus 904. The processor 901 performs various operations of the method flows according to the embodiments of the present disclosure by executing programs in the ROM 902 and/or the RAM 903. Note that the programs may also be stored in one or more memories other than the ROM 902 and the RAM 903. The processor 901 may also perform various operations of the method flows according to embodiments of the present disclosure by executing programs stored in the one or more memories.

Electronic device 900 may also include input/output (I/O) interface 905, input/output (I/O) interface 905 also connected to bus 904, according to an embodiment of the present disclosure. The electronic device 900 may also include one or more of the following components connected to the I/O interface 905: an input portion 906 including a keyboard, a mouse, and the like; an output section 907 including components such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage portion 908 including a hard disk and the like; and a communication section 909 including a network interface card such as a LAN card, a modem, or the like. The communication section 909 performs communication processing via a network such as the internet. The drive 910 is also connected to the I/O interface 905 as necessary. A removable medium 911 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 910 as necessary, so that a computer program read out therefrom is mounted into the storage section 908 as necessary.

The present disclosure also provides a computer-readable storage medium, which may be contained in the apparatus/device/system described in the above embodiments; or may exist separately and not be assembled into the device/apparatus/system. The computer-readable storage medium carries one or more programs which, when executed, implement the method according to an embodiment of the disclosure.

According to embodiments of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, which may include, for example but is not limited to: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. For example, according to embodiments of the present disclosure, a computer-readable storage medium may include the ROM 902 and/or the RAM 903 described above and/or one or more memories other than the ROM 902 and the RAM 903.

Embodiments of the present disclosure also include a computer program product comprising a computer program containing program code for performing the method illustrated in the flow chart. When the computer program product runs in a computer system, the program code is used for causing the computer system to realize the method provided by the embodiment of the disclosure.

The computer program performs the above-described functions defined in the system/apparatus of the embodiments of the present disclosure when executed by the processor 901. The systems, apparatuses, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the present disclosure.

In one embodiment, the computer program may be hosted on a tangible storage medium such as an optical storage device, a magnetic storage device, or the like. In another embodiment, the computer program may also be transmitted, distributed in the form of a signal on a network medium, and downloaded and installed through the communication section 909 and/or installed from the removable medium 911. The computer program containing program code may be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.

In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 909, and/or installed from the removable medium 911. The computer program, when executed by the processor 901, performs the above-described functions defined in the system of the embodiment of the present disclosure. The systems, devices, apparatuses, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the present disclosure.

In accordance with embodiments of the present disclosure, program code for executing computer programs provided by embodiments of the present disclosure may be written in any combination of one or more programming languages, and in particular, these computer programs may be implemented using high level procedural and/or object oriented programming languages, and/or assembly/machine languages. The programming language includes, but is not limited to, programming languages such as Java, C + +, python, the "C" language, or the like. The program code may execute entirely on the user computing device, partly on the user device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

Those skilled in the art will appreciate that various combinations and/or combinations of features recited in the various embodiments and/or claims of the present disclosure can be made, even if such combinations or combinations are not expressly recited in the present disclosure. In particular, various combinations and/or combinations of the features recited in the various embodiments and/or claims of the present disclosure may be made without departing from the spirit or teaching of the present disclosure. All such combinations and/or associations are within the scope of the present disclosure.

The embodiments of the present disclosure have been described above. However, these examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although the embodiments are described separately above, this does not mean that the measures in the embodiments cannot be used in advantageous combination. The scope of the disclosure is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be devised by those skilled in the art without departing from the scope of the present disclosure, and such alternatives and modifications are intended to be within the scope of the present disclosure.

Claims

1. An automatic test case generation method is characterized by comprising the following steps:

s1, performing first extraction on a sample test case based on an extraction unit with preset granularity to obtain a first keyword;

s2, analyzing the first training test case for the first time according to the first keyword;

s3, performing extraction for the (i + 1) th training test case which fails in analysis for (i + 1) th time to obtain an (i + 1) th key word, wherein the extraction for the (i + 1) th time is performed based on an extraction unit with preset granularity, and i is a positive integer greater than or equal to 1;

s4, analyzing the (i + 1) th training test case for (i + 1) th time according to the (i + 1) th keyword;

s5, circularly executing the steps S3-S4 until the proportion of the nth training test case which fails to be analyzed in the nth training test case is smaller than a preset threshold value;

s6, when the proportion of the training test case with failed analysis in the n training test case is smaller than a preset threshold value, acquiring a keyword set, wherein the keyword set comprises the n keyword, n is the number of times of extraction when the proportion of the training test case with failed analysis in the current training test case is smaller than the preset threshold value, and n is a positive integer greater than or equal to i + 1; and

and S7, analyzing the full test cases based on the keyword set to obtain the automatic test cases.

2. The method of claim 1, wherein the parsing the first training test case in accordance with a first keyword comprises:

analyzing the first training test case into a case keyword name and a case input value based on the first keyword, wherein the first keyword comprises the first keyword name and a first keyword input value type;

executing a first judgment, wherein the first judgment comprises judging whether the case keyword name is matched with the first keyword name; and

performing a second determination when the case key name matches the first key name, the second determination including determining whether the case input value matches the first key input value type,

and when the first judgment and the second judgment are matched successfully, determining that the case analysis is successful.

3. The method as claimed in claim 2, wherein the extracting the i +1 times for the i training test case and obtaining the i +1 key word comprises:

analyzing the type of the failure reason based on the ith training test case with analysis failure; and

and performing extraction on the training test case with the analysis failure for the (i + 1) th time based on the failure reason type and a corresponding supplementary extraction method to obtain an (i + 1) th keyword.

4. The method as claimed in claim 3, wherein, when the training test case with the failure cause type including the parsing failure contains a functional module with a larger difference from the functional module corresponding to the ith keyword, the (i + 1) th extraction includes:

and (4) complementarily extracting the (i + 1) th key word on the basis of the functional module with failed analysis.

5. The method of claim 3, wherein when the type of cause of the failed parsing includes that the training test case key name of the failed parsing matches the ith key name, but its case input value does not match the ith key input value type, the (i + 1) th extraction comprises:

and adjusting the keyword input type or a special rule of the newly added keyword input type to obtain the (i + 1) th keyword.

6. The method as claimed in claim 3, wherein, when the type of cause of the parsing failure includes unsuccessful parsing, but the training test case of the parsing failure contains a functional module with a small difference from the functional module corresponding to the ith keyword, the (i + 1) th extraction includes:

and adding the ith keyword for fuzzy matching to form a keyword name group so as to obtain the (i + 1) th keyword.

7. The method of claim 1, wherein prior to parsing a full number of test cases based on the set of keywords to obtain automated test cases, the method further comprises;

and establishing a mapping relation between the keyword set and the automatic test codes, and acquiring an automatic test script.

8. The method of claim 1, wherein the parsing a full number of test cases based on the set of keywords to obtain automated test cases comprises:

recording analysis failure case information;

calculating the ratio of the number of analysis failure cases to the number of analysis completion cases; and

and when the ratio is greater than or equal to a preset threshold value, manually maintaining the keyword set.

9. The method of claim 8, wherein when the ratio is less than the preset threshold, the method further comprises:

and archiving the analyzed full test cases, and performing irregular manual sampling inspection.

10. The method of claim 1, wherein the first extracting the sample test cases based on the preset granularity of the extraction unit comprises:

and when the sample test case statement corresponds to a single page, performing first extraction by taking the sample test case statement as an extraction unit to obtain a first keyword.

11. The method of claim 10, wherein the first extracting the sample test cases in the extracting unit with the preset granularity further comprises:

when the sample test case sentences correspond to m pages, extracting m sub-keywords by taking the sentence part corresponding to each page as an extraction sub-unit; and

packaging the m sub-keywords to obtain a first keyword corresponding to the sample test case statement,

wherein m is a positive integer greater than or equal to 1.

12. An automated test case generation apparatus, comprising:

the first extraction module is configured to perform first extraction on the sample test case based on an extraction unit with preset granularity to obtain a first keyword;

the first analysis module is configured to analyze the first training test case for the first time according to the first keyword;

the second extraction module is configured to perform extraction for the (i + 1) th training test case with failed analysis for (i + 1) th key words, wherein the extraction for the (i + 1) th time is performed based on an extraction unit with preset granularity, and i is a positive integer greater than or equal to 1;

the second analysis module is configured to perform i +1 th analysis on the i +1 th training test case according to the i +1 th keyword;

the circulation module is configured to execute the steps in the second extraction module and the second analysis module in a circulation manner until the ratio of the n training test case with failed analysis in the n training test case is smaller than a preset threshold, wherein n is the extraction times when the ratio of the training test case with failed analysis in the current training test case is smaller than the preset threshold, and n is a positive integer greater than or equal to i + 1;

a keyword obtaining module configured to obtain a keyword set when a proportion of a training test case failing to be analyzed in an nth training test case is less than a preset threshold, the keyword set including an nth keyword,

and

and the testing module is configured to analyze the full number of testing cases based on the keyword set to obtain the automatic testing cases.

13. An electronic device, comprising:

one or more processors;

a storage device for storing one or more programs,

wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the method of any of claims 1-11.

14. A computer readable storage medium having stored thereon executable instructions which, when executed by a processor, cause the processor to perform the method of any one of claims 1 to 11.

15. A computer program product comprising a computer program which, when executed by a processor, implements a method according to any one of claims 1 to 11.